To combat this effect, monocular object detection methods [1]}, [2]}, [3]}, [4]} often learn depth explicitly, by training a monocular depth estimation network in a separate stage. However, depth estimates are consumed directly in the 3D object detection stage without an understanding of depth confidence, leading to networks that tend to be overconfident in depth predictions. Over-confidence in depth is particularly an issue at long range [4]}, leading to poor localization. Further, depth estimation is separated from 3D detection during the training phase, preventing depth map estimates from being optimized for the detection task.
